Author

Abstract

Computational fluid dynamics that accurately simulate highly complex fluid flow situations can generate terabytes of data, greatly increasing the time required for the researcher to fully analyze the data. Presented in this paper is a method to extract shock waves and separation and attachment lines using subjective logic. This method uses software agents that make decisions about extracted features in converging and converged data sets based on belief, disbelief, and uncertainty of extracted data sets. The method uses multiple algorithms for feature detection, accounting for the strengths and weaknesses of each. Using the subjective logic architecture, a final opinion of each extraction is developed. When evaluating an opinion, probability expectation is calculated to interpret how believable an extraction is. The method was validated with simulations of the Onera M6 wing and cylinder in a cross flow. Erroneous extractions have a low probability expectation and believable extractions have a high probability expectation.